1. Field of the Invention
The invention relates to methods and apparatus for estimating relative volumes of tissue types distinguishable in magnetic resonance (MR) images of the tissues.
2. Distinguishing Tissue Types in Brain Images
In vivo estimates of white matter, gray matter and cerebrospinal fluid (CSF) volumes are useful in diagnosis and treatment of several conditions affecting the brain, including Alzheimer's disease, Down's syndrome, senile dementia, and hydrocephalus. In the latter condition, communicating and non-communicating hydrocephalus can be distinguished by estimating CSF volume in images which include the brain ventricles. In other cases, the need for surgical intervention in hydrocephalic patients to establish or repair a CSF drainage shunt can be assessed with serial estimates of CSF contained within the brain ventricles. Diagnosis may also be aided because it has been observed that the ratio of white matter to gray matter in hydrocephalic children is much lower than in control (normal) children.
Computerized tomography (CT) scans and radionuclide ventriculography have been used to estimate ventricular volumes in the brain, but estimate of the error associated with single measurements using either of these techniques are in the range 20-30%. Additionally, reliable in vivo estimates of extraventricular cranial CSF volumes can not be obtained with CT or radionuclide techniques. Reasons for this include the inherently poor resolution of radionuclide techniques and, in the case of CT, the relatively poor contrast of CSF with other brain tissue in CT images.
Superior brain images, on the other hand, are obtained with MR because gray and white matter and CSF are usually visually differentiable in MR images. MR imaging is the first technique to allow in vivo investigations of gross brain structural variation with sufficient resolution to be clinically useful. MR images, however, require careful interpretation due to possible ambiguities in interpretation of the gray scale value of a particular image point or element (pixel).
Ambiguities may arise because a single gray scale pixel value from an MR image of the brain can represent mixed tissue types (e.g., gray matter and CSF). This occurs because of partial volume averaging, in which a given pixel value represents a volume element (voxel) containing more than one tissue type. For example, a given pixel in the image could be classified as 0.5 (50%) gray and 0.5 (50%) white, or 0.4 fluid and 0.6 gray.
The most common techniques to solve this volume averaging problem use some variation of a classification procedure which tries to estimate or find a boundary in the image to allow separation of tissue compartments. Another (rule-based) approach to classifying tissue types requires the choice of two empirical thresholds which may change from subject to subject. Pixel classification in this latter system, therefore, requires a specific model of the data which a particular patient may not fit optimally. Attempts to overcome these problems through use of statistical models and statistical pattern recognition approaches may succeed in avoiding reliance on specific models for the data, but their validity rests on assumptions regarding data distribution which cannot be confirmed in the case of particular patients. Additionally such schemes generally require clinical or operator judgment and this cannot be readily automated.